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vector-embedder

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Generate and store vector embeddings.

Data & Analytics#AI agents#semantic search#data pipeline#RAG#vector embeddings#embedding model
AuthorGreenmamba29
Version1.0.0
Installs0

System Documentation

What problem does it solve?

This Skill automates the creation and storage of vector embeddings, which are crucial for enabling semantic search and AI-driven recommendation systems.

Core Features & Use Cases

  • Text Embedding: Converts text, code, or documents into numerical vector representations.
  • Vector Storage: Persists these embeddings in a chosen vector database (e.g., pgvector, Pinecone, Supabase).
  • Index Creation: Builds or updates vector indexes for efficient similarity searches.
  • Use Case: Embed product descriptions from an e-commerce catalog to power a semantic search feature, allowing users to find products using natural language queries.

Quick Start

Embed all product descriptions in our Supabase catalog for semantic search.

Dependency Matrix

Required Modules

None required

Components

Standard package

💻 Claude Code Installation

Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.

Please help me install this Skill:
Name: vector-embedder
Download link: https://github.com/Greenmamba29/skillsdotmd_web/archive/main.zip#vector-embedder

Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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